Hands-on coding tutorial series for large language models with slides and runnable notebooks covering fine-tuning, prompting, RLHF, safety, steganography, watermarking, multimodal models, GUI agents, and deployment. Community-maintained, free course materials for students and researchers.
Self-hostable answer engine that pairs SearxNG web search with a local (Ollama) or cloud LLM to return cited answers on your own hardware. An open-source Perplexity alternative with Speed, Balanced, and Quality modes plus image, video, and file search.
Runs a privacy-first, self-hosted answering engine that combines web retrieval with local and cloud LLMs to produce cited answers. Supports SearxNG search, file uploads, image/video search, and mix-and-match models with Speed/Balanced/Quality modes.
Provides a cleaned, deduplicated English web corpus optimized for LLM pretraining—over 15T tokens aggregated from CommonCrawl with per-dump snapshots and smaller sampled configs (10B/100B/350B). Includes the datatrove processing pipeline, MinHash deduplication, and an ODC-By v1.0 license; suited for large-scale model training and ablation studies but not specialized for code.
BYOK desktop app working as a universal MCP client: run any MCP server against OpenAI, Anthropic, Gemini, Grok, Ollama and 10+ providers. Also offers prompt-anywhere, AI text commands, local-file RAG, media generation and voice input.
Runs open LLMs entirely on your own machine — discover and download models from Hugging Face, chat in a desktop GUI, or expose an OpenAI-compatible local server. Native Apple MLX and llama.cpp backends; headless deploy via llmster.
Streamlines the full lifecycle of foundation models — data prep, fine-tuning (SFT/LoRA/QLoRA/GRPO), evaluation, and deployment — with ready-to-run recipes, multi-engine inference support, and cloud/CLI workflows for both laptop experiments and large-scale runs.
Splits autonomous R&D into two cooperating agents: one proposes hypotheses, the other writes and tests code — iterating on quant-finance factors, Kaggle pipelines, and model research. Hits a ~30% medal rate on MLE-Bench, nearly double AIDE's.
Provides ~1.3 trillion tokens of web pages filtered for educational quality using an LLM-trained classifier; includes per-Crawl configs, smaller random samples (10B/100B/350B tokens), and the classifier code and model for reproducible filtering.
A community speedrun to train a 124M GPT as fast as possible on 8 H100s, all chasing a fixed 3.28 FineWeb loss. Successive records cut the run from llm.c's 45 minutes to under 1.4, mostly via the new Muon optimizer rather than more hardware.
Ingests documents, images, audio, video and web pages and converts them into structured, LLM-friendly markdown and parsed data. Runs locally (fits on a T4 GPU), supports ~20 file types, offers OCR, transcription, table extraction and a Gradio UI; deployable via Docker/Skypilot. Licensed under GPL-3.0; some model weights carry cc-by-nc-sa restrictions for commercial use.